IEEE ACCESS SPECIAL SECTION EDITORIAL: ADVANCED DATA MINING METHODS FOR SOCIAL COMPUTING

被引：1

作者：

Zhao, Yongqiang ^{[1
]}

Pan, Shirui ^{[2
]}

Wu, Jia ^{[3
]}

Wan, Huaiyu ^{[4
]}

Liang, Huizhi ^{[5
]}

Wang, Haishuai ^{[6
]}

Shen, Huawei ^{[7
]}

机构：

[1] Northwestern Polytech Univ, Sch Automat, Xian 710129, Peoples R China

[2] Monash Univ, Dept Data Sci & AI, Melbourne, Vic 3800, Australia

[3] Macquarie Univ, Dept Comp, Sydney, NSW 2109, Australia

[4] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China

[5] Univ Reading, Dept Comp Sci, Reading RG6 6AH, Berks, England

[6] Harvard Med Sch, Dept Biomed Informat, Boston, MA 02115 USA

[7] Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China

来源：

IEEE ACCESS | 2020年 / 8卷

关键词：

D O I：

10.1109/ACCESS.2020.3043060

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Various kinds of social networks develop explosively, such as online social networks, scientific cooperation networks, athlete networks, airport passage networks, and so on. With the large number of participants and real-time property, social networks increasingly demonstrate their strength in information dissemination. Social computing has become a promising research area and attracts lots of attention. Analyzing and mining human behaviors, topological structure, and information diffusion in social networks can help to understand the essential mechanism of macroscopic phenomena, discover potential public interest, and provide early warnings of collective emergencies.

引用

页码：228598 / 228604

页数：7